Speed and consistency of target-shifting play a crucial role in human ability to perform complex tasks. Shifting our gaze between objects of interest quickly and consistently requires changes both in depth and direction. Gaze changes in depth are driven by slow, inconsistent vergence movements which rotate the eyes in opposite directions, while changes in direction are driven by ballistic, consistent movements called saccades, which rotate the eyes in the same direction. In the natural world, most of our eye movements are a combination of both types. While scientific consensus on the nature of saccades exists, vergence and combined movements remain less understood and agreed upon. We eschew the lack of scientific consensus in favor of proposing an operationalized computational model which predicts the speed of any type of gaze movement during target-shifting in 3D. To this end, we conduct a psychophysical study in a stereo VR environment to collect more than 12,000 gaze movement trials, analyze the temporal distribution of the observed gaze movements, and fit a probabilistic model to the data. We perform a series of objective measurements and user studies to validate the model. The results demonstrate its predictive accuracy, generalization, as well as applications for optimizing visual performance by altering content placement. Lastly, we leverage the model to measure differences in human target-changing time relative to the natural world, as well as suggest scene-aware projection depth. By incorporating the complexities and randomness of human oculomotor control, we hope this research will support new behavior-aware metrics for VR/AR display design, interface layout, and gaze-contingent rendering.

翻译：目标切换的速度与一致性对人类执行复杂任务的能力至关重要。在感兴趣物体之间快速且一致地转移注视点需要同时改变深度和方向。深度注视变化由缓慢且不一致的辐辏运动驱动（双眼反向旋转），而方向注视变化则由弹道式且一致的扫视运动驱动（双眼同向旋转）。自然世界中，大多数眼动是这两种类型的组合。尽管科学界对扫视的本质已形成共识，但对辐辏运动及其复合运动的认知仍不明确且存在争议。我们避开科学共识的缺失，提出一种可操作的计算模型，用于预测三维空间中目标切换时各类眼动速度。为此，我们在立体VR环境中开展心理物理学实验，收集超过12,000次眼动试验数据，分析观测到的眼动时间分布，并拟合概率模型。通过系列客观测量与用户研究验证该模型，结果表明其具有预测准确性、泛化能力，以及通过调整内容布局优化视觉表现的应用价值。最后，我们利用该模型量化人类在自然世界中的目标切换时间差异，并提出场景感知投影深度的建议。通过纳入人类眼动控制的复杂性与随机性，希望本研究能为VR/AR显示设计、界面布局及注视点渲染提供新型行为感知评估指标。